AIM Score vs. Gene Expression
Full X range:
Auto X range:
Group Comparisons: Boxplots
CP73
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 1.064 | 0.315 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.706 |
| Model: | OLS | Adj. R-squared: | 0.660 |
| Method: | Least Squares | F-statistic: | 15.21 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 2.76e-05 |
| Time: | 23:00:01 | Log-Likelihood: | -99.028 |
| No. Observations: | 23 | AIC: | 206.1 |
| Df Residuals: | 19 | BIC: | 210.6 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 68.6764 | 218.029 | 0.315 | 0.756 | -387.664 525.017 |
| C(dose)[T.1] | -548.1660 | 376.056 | -1.458 | 0.161 | -1335.260 238.928 |
| expression | -1.6724 | 25.194 | -0.066 | 0.948 | -54.403 51.058 |
| expression:C(dose)[T.1] | 68.3197 | 42.946 | 1.591 | 0.128 | -21.567 158.206 |
| Omnibus: | 0.602 | Durbin-Watson: | 2.234 |
| Prob(Omnibus): | 0.740 | Jarque-Bera (JB): | 0.683 |
| Skew: | 0.291 | Prob(JB): | 0.711 |
| Kurtosis: | 2.389 | Cond. No. | 982. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.667 |
| Model: | OLS | Adj. R-squared: | 0.633 |
| Method: | Least Squares | F-statistic: | 20.01 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 1.69e-05 |
| Time: | 23:00:01 | Log-Likelihood: | -100.47 |
| No. Observations: | 23 | AIC: | 206.9 |
| Df Residuals: | 20 | BIC: | 210.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -134.7281 | 183.237 | -0.735 | 0.471 | -516.953 247.497 |
| C(dose)[T.1] | 49.9112 | 9.168 | 5.444 | 0.000 | 30.787 69.035 |
| expression | 21.8393 | 21.169 | 1.032 | 0.315 | -22.319 65.998 |
| Omnibus: | 1.561 | Durbin-Watson: | 2.148 |
| Prob(Omnibus): | 0.458 | Jarque-Bera (JB): | 1.097 |
| Skew: | 0.259 | Prob(JB): | 0.578 |
| Kurtosis: | 2.064 | Cond. No. | 380. |
Model:
AIM ~ C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.649 |
| Model: | OLS | Adj. R-squared: | 0.632 |
| Method: | Least Squares | F-statistic: | 38.84 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 3.51e-06 |
| Time: | 23:00:01 | Log-Likelihood: | -101.06 |
| No. Observations: | 23 | AIC: | 206.1 |
| Df Residuals: | 21 | BIC: | 208.4 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 54.2083 | 5.919 | 9.159 | 0.000 | 41.900 66.517 |
| C(dose)[T.1] | 53.3371 | 8.558 | 6.232 | 0.000 | 35.539 71.135 |
| Omnibus: | 0.322 | Durbin-Watson: | 1.888 |
| Prob(Omnibus): | 0.851 | Jarque-Bera (JB): | 0.485 |
| Skew: | 0.060 | Prob(JB): | 0.785 |
| Kurtosis: | 2.299 | Cond. No. | 2.57 |
Model:
AIM ~ expression
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.173 |
| Model: | OLS | Adj. R-squared: | 0.134 |
| Method: | Least Squares | F-statistic: | 4.393 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0484 |
| Time: | 23:00:01 | Log-Likelihood: | -110.92 |
| No. Observations: | 23 | AIC: | 225.8 |
| Df Residuals: | 21 | BIC: | 228.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -475.1357 | 264.805 | -1.794 | 0.087 | -1025.827 75.555 |
| expression | 63.5845 | 30.336 | 2.096 | 0.048 | 0.496 126.673 |
| Omnibus: | 2.103 | Durbin-Watson: | 3.143 |
| Prob(Omnibus): | 0.349 | Jarque-Bera (JB): | 1.089 |
| Skew: | -0.018 | Prob(JB): | 0.580 |
| Kurtosis: | 1.935 | Cond. No. | 357. |
CP101
Model Comparison: AIM ~ expression + C(dose) vs AIM ~ C(dose)
| F-statistic | p-value | df difference |
| 0.424 | 0.527 | 1.0 |
Model:
AIM ~ expression + C(dose) + expression:C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.504 |
| Model: | OLS | Adj. R-squared: | 0.368 |
| Method: | Least Squares | F-statistic: | 3.718 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0456 |
| Time: | 23:00:01 | Log-Likelihood: | -70.049 |
| No. Observations: | 15 | AIC: | 148.1 |
| Df Residuals: | 11 | BIC: | 150.9 |
| Df Model: | 3 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -379.0883 | 419.058 | -0.905 | 0.385 | -1301.428 543.251 |
| C(dose)[T.1] | 632.8273 | 649.293 | 0.975 | 0.351 | -796.257 2061.911 |
| expression | 49.3928 | 46.338 | 1.066 | 0.309 | -52.597 151.383 |
| expression:C(dose)[T.1] | -64.9101 | 72.774 | -0.892 | 0.392 | -225.085 95.265 |
| Omnibus: | 2.220 | Durbin-Watson: | 1.294 |
| Prob(Omnibus): | 0.329 | Jarque-Bera (JB): | 1.539 |
| Skew: | -0.758 | Prob(JB): | 0.463 |
| Kurtosis: | 2.591 | Cond. No. | 962. |
Model:
AIM ~ expression + C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.468 |
| Model: | OLS | Adj. R-squared: | 0.379 |
| Method: | Least Squares | F-statistic: | 5.270 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.0228 |
| Time: | 23:00:01 | Log-Likelihood: | -70.572 |
| No. Observations: | 15 | AIC: | 147.1 |
| Df Residuals: | 12 | BIC: | 149.3 |
| Df Model: | 2 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | -141.1798 | 320.443 | -0.441 | 0.667 | -839.365 557.005 |
| C(dose)[T.1] | 53.9016 | 17.072 | 3.157 | 0.008 | 16.705 91.098 |
| expression | 23.0759 | 35.425 | 0.651 | 0.527 | -54.108 100.260 |
| Omnibus: | 3.231 | Durbin-Watson: | 1.018 |
| Prob(Omnibus): | 0.199 | Jarque-Bera (JB): | 2.030 |
| Skew: | -0.897 | Prob(JB): | 0.362 |
| Kurtosis: | 2.833 | Cond. No. | 376. |
Model:
AIM ~ C(dose)
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.449 |
| Model: | OLS | Adj. R-squared: | 0.406 |
| Method: | Least Squares | F-statistic: | 10.58 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.00629 |
| Time: | 23:00:02 | Log-Likelihood: | -70.833 |
| No. Observations: | 15 | AIC: | 145.7 |
| Df Residuals: | 13 | BIC: | 147.1 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 67.4286 | 11.044 | 6.106 | 0.000 | 43.570 91.287 |
| C(dose)[T.1] | 49.1964 | 15.122 | 3.253 | 0.006 | 16.527 81.866 |
| Omnibus: | 2.713 | Durbin-Watson: | 0.810 |
| Prob(Omnibus): | 0.258 | Jarque-Bera (JB): | 1.868 |
| Skew: | -0.843 | Prob(JB): | 0.393 |
| Kurtosis: | 2.619 | Cond. No. | 2.70 |
Model:
AIM ~ expression
OLS Regression Results
| Dep. Variable: | AIM | R-squared: | 0.025 |
| Model: | OLS | Adj. R-squared: | -0.050 |
| Method: | Least Squares | F-statistic: | 0.3377 |
| Date: | Thu, 03 Apr 2025 | Prob (F-statistic): | 0.571 |
| Time: | 23:00:02 | Log-Likelihood: | -75.108 |
| No. Observations: | 15 | AIC: | 154.2 |
| Df Residuals: | 13 | BIC: | 155.6 |
| Df Model: | 1 | | |
| coef | std err | t | P>|t| | [95.0% Conf. Int.] |
| Intercept | 310.2279 | 372.804 | 0.832 | 0.420 | -495.165 1115.621 |
| expression | -24.2473 | 41.726 | -0.581 | 0.571 | -114.390 65.896 |
| Omnibus: | 0.163 | Durbin-Watson: | 1.356 |
| Prob(Omnibus): | 0.922 | Jarque-Bera (JB): | 0.370 |
| Skew: | -0.091 | Prob(JB): | 0.831 |
| Kurtosis: | 2.253 | Cond. No. | 336. |